WEB DEVELOPER SITE
HTMLCSSJAVASCRIPTSQLPHPBOOTSTRAPJQUERYANGULARXML
 

MongoDB - Map Reduce


In this chapter we discuss MongoDB - Map Reduce

Map-reduce is a data processing paradigm for condensing large volumes of data into useful aggregated results.

MongoDB uses mapReduce command for map-reduce operations. MapReduce is generally used for processing large data sets.

MapReduce Command

>db.collection.mapReduce(
   function() {emit(key,value);},  //map function
   function(key,values) {return reduceFunction}, {   //reduce function
      out: collection,
      query: document,
      sort: document,
      limit: number
   }
)
There are following syntax -
  • map is a javascript function that maps a value with a key and emits a key-value pair

  • reduce is a javascript function that reduces or groups all the documents having the same key

  • out specifies the location of the map-reduce query result

  • query specifies the optional selection criteria for selecting documents

  • sort specifies the optional sort criteria

  • limit specifies the optional maximum number of documents to be returned

Using MapReduce

Consider the following document structure storing user posts. The document stores user_name of the user and the status of post.
{
   "post_text": "Angeline Jolie is super Hollywood actress ",
   "user_name": "angle",
   "status":"active"
}

we will use a mapReduce function on our posts collection to select all the active posts, group them on the basis of user_name and then count the number of posts by each user using the following code
>db.posts.mapReduce( 
   function() { emit(this.user_id,1); }, 
	
   function(key, values) {return Array.sum(values)}, {  
      query:{status:"active"},  
      out:"post_total" 
   }
)

The above mapReduce query outputs the following result
{
   "result" : "post_total",
   "timeMillis" : 9,
   "counts" : {
      "input" : 4,
      "emit" : 4,
      "reduce" : 2,
      "output" : 2
   },
   "ok" : 1,
}

The result shows that a total of 4 documents matched the query (status:"active"), the map function emitted 4 documents with key-value pairs and finally the reduce function grouped mapped documents having the same keys into 2.

To see the result of this mapReduce query, use the find operator
>db.posts.mapReduce( 
   function() { emit(this.user_id,1); }, 
   function(key, values) {return Array.sum(values)}, {  
      query:{status:"active"},  
      out:"post_total" 
   }
	
).find()
The above query gives the following result which indicates that both users Peterson and Dewayne have two posts in active states
{ "_id" : "Peterson", "value" : 2 }
{ "_id" : "Dewayne", "value" : 2 }
a similar manner, MapReduce queries can be used to construct large complex aggregation queries. The use of custom Javascript functions make use of MapReduce which is very flexible and powerful.



Welookups is optimized for learning.Copy right 2018 kumar aditya singh .
All Right Reserved and you agree to have read and accepted our term and condition.
All Rights Reserved.